class_docstr: This class decodes each item in a batch by resizing each image
  window and save as a new image volume. Multiple output image can be
  proposed and csv output can be performed as well
class_docstr_tok: ["This","class","decodes","each","item","in","a","batch","by","resizing","each","image","window","and","save","as","a","new","image","volume",".","Multiple","output","image","can","be","proposed","and","csv","output","can","be","performed","as","well"]
docstr: Initialise the array to be saved as csv as a line of zeros according
  to the number of elements to be saved
  :param n_channel:
  :return:
docstr_tok: ["Initialise","the","array","to","be","saved","as","csv","as","a","line","of","zeros","according","to","the","number","of","elements","to","be","saved",":","param","n_channel",":",":","return",":"]
code: def _initialise_empty_csv(self, key_names):
      """
      Initialise the array to be saved as csv as a line of zeros according
      to the number of elements to be saved
      :param n_channel:
      :return:
      """
      return pd.DataFrame(columns=key_names)
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_resize.py
imports: from __future__ import absolute_import, division, print_function
  import os
  from collections import OrderedDict
  import numpy
  import pandas
  import niftynet.io.misc_io
  from niftynet.engine.sampler_resize_v2 import zoom_3d
  from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 6 for calling the ResizeSamplesAggregator obj's underlying member method code with example usage: obj._initialise_empty_csv(key_names) and returns: pd